Splicing efficiency of minor introns in a mouse model of SMA predominantly depends on their branchpoint sequence and can involve the contribution of major spliceosome components.

Spinal Muscular Atrophy (SMA) is a devastating neurodegenerative disease caused by reduced amounts of the ubiquitously expressed Survival of Motor Neuron (SMN) protein. In agreement with its crucial role in the biogenesis of spliceosomal snRNPs, SMN-deficiency is correlated to numerous splicing alterations in patient cells and various tissues of SMA mouse models. Among the snRNPs whose assembly is impacted by SMN-deficiency, those involved in the minor spliceosome are particularly affected. Importantly, splicing of several, but not all U12-dependent introns has been shown to be affected in different SMA models. Here, we have investigated the molecular determinants of this differential splicing in spinal cords from SMA mice. We show that the branchpoint sequence (BPS) is a key element controlling splicing efficiency of minor introns. Unexpectedly, splicing of several minor introns with suboptimal BPS is not affected in SMA mice. Using in vitro splicing experiments and oligonucleotides targeting minor or major snRNAs, we show for the first time that splicing of these introns involves both the minor and major machineries. Our results strongly suggest that splicing of a subset of minor introns is not affected in SMA mice because components of the major spliceosome compensate for the loss of minor splicing activity.

RNA splicing analysis using heterogeneous and large RNA-seq1datasets

The ubiquity of RNA-seq has led to many methods that use RNA-seq data to analyze variations in RNA splicing. However, available methods are not well suited for handling heterogeneous and large datasets. Such datasets scale to housands of samples across dozens of experimental conditions, exhibit increased variability compared to biological replicates, and involve thousands of unannotated splice variants resulting in increased transcriptome complexity. We describe here a suite of algorithms and tools implemented in the MAJIQ v2 package to address challenges in detection, quantification, and visualization of splicing variations from such datasets. Using both large scale synthetic data and GTEx v8 as benchmark datasets, we demonstrate that the approaches in MAJIQ v2 outperform existing methods. We then apply MAJIQ v2 package to analyze differential splicing across 2,335 samples from 13 brain subregions, demonstrating its ability to offer new insights into brain subregion-specific splicing regulation.

Maturation State-Specific Alternative Splicing in FLT3-ITD and NPM1 Mutated AML

Despite substantial progress achieved in unraveling the genetics of AML in the past decade, its treatment outcome has not substantially improved. Therefore, it is important to better understand how genetic mutations translate to phenotypic features of AML cells to further improve response predictions and to find innovative therapeutic approaches. In this respect, aberrant splicing is a crucial contributor to the pathogenesis of hematological malignancies. Thus far, altered splicing is well characterized in relation to splicing factor mutations in AML. However, splicing profiles associated with mutations in other genes remain largely unexplored. In this study, we explored differential splicing profiles associated with two of the most common aberrations in AML: FLT3-ITD and NPM1 mutations. Using RNA-sequencing data of a total of 382 primary AML samples, we found that the co-occurrence of FLT3-ITD and mutated NPM1 is associated with differential splicing of FAB-type specific gene sets. Despite the FAB-type specificity of particular gene sets, the primary functions perturbed by differential splicing in all three FAB types include cell cycle control and DNA damage response. Interestingly, we observed functional divergence between alternatively spliced and differentially expressed genes in FLT3-ITD+/NPM1+ samples in all analyzed FAB types, with differential expression affecting genes involved in hematopoietic differentiation. Altogether, these observations indicate that concomitant FLT3-ITD and mutated NPM1 are associated with the maturation state-specific differential splicing of genes with potential oncogenic relevance.

Long-read transcriptome sequencing analysis with IsoTools

Long-read transcriptome sequencing (LRTS) holds the promise to boost our understanding of alternative splicing. Recent advances in accuracy and throughput have diminished the major limitations and enabled the direct quantification of isoforms. Considering the complexity of the data and the broad range of potential applications, it is clear that highly flexible, accurate analysis tools are crucial. Here, we present IsoTools, a comprehensive Python-based analysis package, for the improvement of alternative and differential splicing analysis. IsoTools provides a comprehensive data structure that integrates genomic information from LRTS transcripts together with the reference annotation, and enables broad functionality to quality control, visualize and analyze the data. Additionally, we implemented a graph-based method for the identification of alternative splicing events and a statistical approach based on the beta binomial distribution for the detection of differential events. To demonstrate our methods, we generated PacBio Iso-Seq data of human hepatocytes treated with the HDAC inhibitor valproic acid, a compound known to induce widespread transcriptional changes. Contrasted with short read RNA-Seq of the same samples, this analysis shows that LRTS provides valuable additional insights for a better understanding of alternative splicing, in particular with respect to complex novel and differential splicing events. IsoTools is made available for the community along with extensive documentation at https://github.com/MatthiasLienhard/isotools.

Targeting KRAS4A splicing through the RBM39/DCAF15 pathway inhibits cancer stem cells

The commonly mutated human KRAS oncogene encodes two distinct KRAS4A and KRAS4B proteins generated by differential splicing. We demonstrate here that coordinated regulation of both isoforms through control of splicing is essential for development of Kras mutant tumors. The minor KRAS4A isoform is enriched in cancer stem-like cells, where it responds to hypoxia, while the major KRAS4B is induced by ER stress. KRAS4A splicing is controlled by the DCAF15/RBM39 pathway, and deletion of KRAS4A or pharmacological inhibition of RBM39 using Indisulam leads to inhibition of cancer stem cells. Our data identify existing clinical drugs that target KRAS4A splicing, and suggest that levels of the minor KRAS4A isoform in human tumors can be a biomarker of sensitivity to some existing cancer therapeutics.

Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers

Background Computational tools analyzing RNA-sequencing data have boosted alternative splicing research by identifying and assessing differentially spliced genes. However, common alternative splicing analysis tools differ substantially in their statistical analyses and general performance. This report compares the computational performance (CPU utilization and RAM usage) of three event-level splicing tools; rMATS, MISO, and SUPPA2. Additionally, concordance between tool outputs was investigated. Results Log-linear relations were found between job times and dataset size in all splicing tools and all virtual machine (VM) configurations. MISO had the highest job times for all analyses, irrespective of VM size, while MISO analyses also exceeded maximum CPU utilization on all VM sizes. rMATS and SUPPA2 load averages were relatively low in both size and replicate comparisons, not nearing maximum CPU utilization in the VM simulating the lowest computational power (D2 VM). RAM usage in rMATS and SUPPA2 did not exceed 20% of maximum RAM in both size and replicate comparisons while MISO reached maximum RAM usage in D2 VM analyses for input size. Correlation coefficients of differential splicing analyses showed high correlation (β > 80%) between different tool outputs with the exception of comparisons of retained intron (RI) events between rMATS/MISO and rMATS/SUPPA2 (β < 60%). Conclusions Prior to RNA-seq analyses, users should consider job time, amount of replicates and splice event type of interest to determine the optimal alternative splicing tool. In general, rMATS is superior to both MISO and SUPPA2 in computational performance. Analysis outputs show high concordance between tools, with the exception of RI events.

Complexity and graded regulation of neuronal cell-type–specific alternative splicing revealed by single-cell RNA sequencing

The enormous cellular diversity in the mammalian brain, which is highly prototypical and organized in a hierarchical manner, is dictated by cell-type–specific gene-regulatory programs at the molecular level. Although prevalent in the brain, the contribution of alternative splicing (AS) to the molecular diversity across neuronal cell types is just starting to emerge. Here, we systematically investigated AS regulation across over 100 transcriptomically defined neuronal types of the adult mouse cortex using deep single-cell RNA-sequencing data. We found distinct splicing programs between glutamatergic and GABAergic neurons and between subclasses within each neuronal class. These programs consist of overlapping sets of alternative exons showing differential splicing at multiple hierarchical levels. Using an integrative approach, our analysis suggests that RNA-binding proteins (RBPs) Celf1/2, Mbnl2, and Khdrbs3 are preferentially expressed and more active in glutamatergic neurons, while Elavl2 and Qk are preferentially expressed and more active in GABAergic neurons. Importantly, these and additional RBPs also contribute to differential splicing between neuronal subclasses at multiple hierarchical levels, and some RBPs contribute to splicing dynamics that do not conform to the hierarchical structure defined by the transcriptional profiles. Thus, our results suggest graded regulation of AS across neuronal cell types, which may provide a molecular mechanism to specify neuronal identity and function that are orthogonal to established classifications based on transcriptional regulation.

Single-cell differential splicing analysis reveals high heterogeneity of liver tumor-infiltrating T cells

Recent advances in single-cell RNA sequencing (scRNA-seq) have improved our understanding of the association between tumor-infiltrating lymphocyte (TILs) heterogeneity and cancer initiation and progression. However, studies investigating alternative splicing (AS) as an important regulatory factor of heterogeneity remain limited. Here, we developed a new computational tool, DESJ-detection, which accurately detects differentially expressed splicing junctions (DESJs) between cell groups at the single-cell level. We analyzed 5063 T cells of hepatocellular carcinoma (HCC) and identified 1176 DESJs across 11 T cell subtypes. Interestingly, DESJs were enriched in UTRs, and have putative effects on heterogeneity. Cell subtypes with a similar function closely clustered together at the AS level. Meanwhile, we identified a novel cell state, pre-activation with the isoform markers ARHGAP15-205. In summary, we present a comprehensive investigation of alternative splicing differences, which provided novel insights into T cell heterogeneity and can be applied to other full-length scRNA-seq datasets.

Complexity and graded regulation of neuronal cell type-specific alternative splicing revealed by single-cell RNA sequencing

The enormous neuronal cellular diversity in the mammalian brain, which is highly prototypical and organized in a hierarchical manner, is dictated by cell type-specific gene regulatory programs at the molecular level. Although prevalent in the brain, contribution of alternative splicing (AS) to the molecular diversity across neuronal cell types is just starting to emerge. Here we systematically investigated AS regulation across over 100 transcriptomically defined neuronal types of the adult mouse cortex using deep single cell RNA-sequencing (scRNA-seq) data. We found distinct splicing programs between glutamatergic and GABAergic neurons and between subclasses within each neuronal class, consisting of overlapping sets of alternative exons showing differential splicing at multiple hierarchical levels. Using an integrative approach, our analysis suggests that RNA-binding proteins (RBPs) Celf1/2, Mbnl2 and Khdrbs3 are preferentially expressed and more active in glutamatergic neurons, while Elavl2 and Qk are preferentially expressed and more active in GABAergic neurons. Importantly, these and additional RBPs also contribute to differential splicing between neuronal subclasses at multiple hierarchical levels, and some RBPs drive splicing dynamics that do not conform to the hierarchical structure defined by the transcriptional profiles. Thus, our results suggest graded regulation of AS across neuronal cell types, which provides a molecular mechanism orthogonal to, rather than downstream of, transcriptional regulation in specifying neuronal identity and function.SignificanceAlternative splicing (AS) is extensively used in the mammalian brain, but its contribution to the molecular and cellular diversity across neuronal cell types remains poorly understood. Through systematic and integrative analysis of AS regulation across over 100 transcriptomically defined cortical neuronal types, we found neuronal subclass-specific splicing regulatory programs consists of overlapping alternative exons showing differential splicing at multiple hierarchical levels. This graded AS regulation is controlled by unique combinations of RNA-binding proteins (RBPs). Importantly, these RBPs also drive splicing dynamics across neuronal cell types that do not conform to the hierarchical taxonomy established based on transcriptional profiles, suggesting that the graded AS regulation provides a molecular mechanism orthogonal to transcriptional regulation in specifying neuronal identity and function.